IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v274y2019i3p978-989.html
   My bibliography  Save this article

Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method

Author

Listed:
  • Li, An-Da
  • He, Zhen
  • Wang, Qing
  • Zhang, Yang

Abstract

The selection of key quality characteristics (KQCs) that are significantly associated with product quality is essential for improving product quality. Production lines generally yield a larger number of regular products than do premium products, which creates an imbalance in production datasets and complicates KQC selection. In this study, a KQC selection method with an excellent ability to predict product quality is proposed based on a two-phase bi-objective feature selection method. The KQC selection model is established as a bi-objective problem of maximizing feature (i.e., quality characteristic) importance and minimizing percentage of selected features, and the geometric mean (G-mean) is selected as the feature importance metric for imbalanced data. To solve this model, a two-phase multi-objective optimization method is proposed; this method yields a set of candidate solutions (KQC sets) using an improved direct multi-search (DMS) strategy and uses the ideal point method (IPM) to select the final KQC sets from the candidate solutions. The experimental results indicate that the proposed method is effective for selecting KQCs for imbalanced production data.

Suggested Citation

  • Li, An-Da & He, Zhen & Wang, Qing & Zhang, Yang, 2019. "Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method," European Journal of Operational Research, Elsevier, vol. 274(3), pages 978-989.
    • Handle: RePEc:eee:ejores:v:274:y:2019:i:3:p:978-989
    DOI: 10.1016/j.ejor.2018.10.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718309196
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.10.051?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Anzanello, Michel J. & Albin, Susan L. & Chaovalitwongse, Wanpracha A., 2012. "Multicriteria variable selection for classification of production batches," European Journal of Operational Research, Elsevier, vol. 218(1), pages 97-105.
    2. Du, Wen Sheng & Hu, Bao Qing, 2018. "A fast heuristic attribute reduction approach to ordered decision systems," European Journal of Operational Research, Elsevier, vol. 264(2), pages 440-452.
    3. Unler, Alper & Murat, Alper, 2010. "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, Elsevier, vol. 206(3), pages 528-539, November.
    4. Ghaddar, Bissan & Naoum-Sawaya, Joe, 2018. "High dimensional data classification and feature selection using support vector machines," European Journal of Operational Research, Elsevier, vol. 265(3), pages 993-1004.
    5. Maldonado, Sebastián & Pérez, Juan & Bravo, Cristián, 2017. "Cost-based feature selection for Support Vector Machines: An application in credit scoring," European Journal of Operational Research, Elsevier, vol. 261(2), pages 656-665.
    6. Wen-meng Tian & Zhen He & Wei Yan, 2013. "Key Process Variable Identification for Quality Classification Based on PLSR Model and Wrapper Feature Selection," Springer Books, in: Runliang Dou (ed.), Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012), edition 127, chapter 0, pages 263-270, Springer.
    7. M. Freimer & P. L. Yu, 1976. "Some New Results on Compromise Solutions for Group Decision Problems," Management Science, INFORMS, vol. 22(6), pages 688-693, February.
    8. Bertolazzi, P. & Felici, G. & Festa, P. & Fiscon, G. & Weitschek, E., 2016. "Integer programming models for feature selection: New extensions and a randomized solution algorithm," European Journal of Operational Research, Elsevier, vol. 250(2), pages 389-399.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiménez-Cordero, Asunción & Morales, Juan Miguel & Pineda, Salvador, 2021. "A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification," European Journal of Operational Research, Elsevier, vol. 293(1), pages 24-35.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Yishi & Zhu, Ruilin & Chen, Zhijun & Gao, Jie & Xia, De, 2021. "Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data," European Journal of Operational Research, Elsevier, vol. 290(1), pages 235-247.
    2. Lee, In Gyu & Yoon, Sang Won & Won, Daehan, 2022. "A Mixed Integer Linear Programming Support Vector Machine for Cost-Effective Group Feature Selection: Branch-Cut-and-Price Approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1055-1068.
    3. Jiménez-Cordero, Asunción & Morales, Juan Miguel & Pineda, Salvador, 2021. "A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification," European Journal of Operational Research, Elsevier, vol. 293(1), pages 24-35.
    4. Yu, Shiwei & Wei, Yi-Ming & Fan, Jingli & Zhang, Xian & Wang, Ke, 2012. "Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization," Applied Energy, Elsevier, vol. 92(C), pages 552-562.
    5. Moraes, Marcelo Botelho da Costa & Nagano, Marcelo Seido, 2014. "Evolutionary models in cash management policies with multiple assets," Economic Modelling, Elsevier, vol. 39(C), pages 1-7.
    6. Hsu, C.-H. & Wang, Fu-Kwun & Tzeng, Gwo-Hshiung, 2012. "The best vendor selection for conducting the recycled material based on a hybrid MCDM model combining DANP with VIKOR," Resources, Conservation & Recycling, Elsevier, vol. 66(C), pages 95-111.
    7. Gao, Zheming & Fang, Shu-Cherng & Luo, Jian & Medhin, Negash, 2021. "A kernel-free double well potential support vector machine with applications," European Journal of Operational Research, Elsevier, vol. 290(1), pages 248-262.
    8. Bas Dietzenbacher & Hans Peters, 2022. "Characterizing NTU-bankruptcy rules using bargaining axioms," Annals of Operations Research, Springer, vol. 318(2), pages 871-888, November.
    9. Astorino, Annabella & Avolio, Matteo & Fuduli, Antonio, 2022. "A maximum-margin multisphere approach for binary Multiple Instance Learning," European Journal of Operational Research, Elsevier, vol. 299(2), pages 642-652.
    10. Claus-Jochen Haake & Cheng-Zhong Qin, 2018. "On unification of solutions to the bargaining problem," Working Papers CIE 113, Paderborn University, CIE Center for International Economics.
    11. Stokes, Jeffrey R. & Tozer, Peter R., 2002. "Sire selection with multiple objectives," Agricultural Systems, Elsevier, vol. 73(2), pages 147-164, August.
    12. Sandra Benítez-Peña & Rafael Blanquero & Emilio Carrizosa & Pepa Ramírez-Cobo, 2019. "On support vector machines under a multiple-cost scenario," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 663-682, September.
    13. Blanquero, R. & Carrizosa, E. & Jiménez-Cordero, A. & Martín-Barragán, B., 2019. "Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm," European Journal of Operational Research, Elsevier, vol. 275(1), pages 195-207.
    14. Manlio Gaudioso & Giovanni Giallombardo & Giovanna Miglionico, 2023. "Sparse optimization via vector k-norm and DC programming with an application to feature selection for support vector machines," Computational Optimization and Applications, Springer, vol. 86(2), pages 745-766, November.
    15. Yongjun Li & Xiao Shi & Min Yang & Liang Liang, 2017. "Variable selection in data envelopment analysis via Akaike’s information criteria," Annals of Operations Research, Springer, vol. 253(1), pages 453-476, June.
    16. Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.
    17. Jingjing Long & Cuiqing Jiang & Stanko Dimitrov & Zhao Wang, 2022. "Clues from networks: quantifying relational risk for credit risk evaluation of SMEs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-41, December.
    18. Basna Mohammed Salih Hasan & Nawzat Sadiq Ahmed, 2021. "Feature selection technique applied in Medical application by Supervised algorithm: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 190-203.
    19. Jiaming Liu & Jiajia Liu & Chong Wu & Shouyang Wang, 2024. "Enhancing credit risk prediction based on ensemble tree‐based feature transformation and logistic regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 429-455, March.
    20. Bin, Wei & Qinke, Peng & Jing, Zhao & Xiao, Chen, 2012. "A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior," European Journal of Operational Research, Elsevier, vol. 219(2), pages 224-233.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:274:y:2019:i:3:p:978-989. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.